Email suggestor system

ABSTRACT

The email suggestor system and method provide an efficient and effective way to capture a user identifier, such as an email address of a consumer in a retail environment. The email suggestor system generates one or more suggested first text portions based on input data, outputs at least one of the suggested first text portions, and receives a selection of a first text portion. The email suggestor system generates one or more suggested second text portions of a user identifier based on the input data, outputs at least one of the suggested second text portions, and receives a selection of a second text portion. The email suggestor system generates a user identifier including the selected first text portion and the selected second text portion. The email suggestor system uses received feedback response to refine and/or train one or more models with which it generates the suggested text portions.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.14/958,423, filed Dec. 3, 2015, which is a continuation of U.S.application Ser. No. 13/744,141, filed Jan. 17, 2013, now U.S. Pat. No.9,223,774, issued Dec. 29, 2015, which claims priority to and thebenefit of U.S. Provisional Application No. 61/587,516, filed Jan. 17,2012, the entire contents of each are hereby incorporated by referencein their entirety.

TECHNICAL FIELD

The present description relates to an efficient and effective way tocapture consumer contact information. This description more specificallyrelates to improving the consumer experience and minimizing thetransaction time to capture a consumer's email username and domain nameby a merchant in a retail environment.

BACKGROUND

Merchants typically wish to obtain a consumer's e-mail address. One wayto obtain the e-mail address is to rely on the consumer to input theaddress. However, the consumer may find inputting the e-mail addresstedious and inconvenient. Another way to obtain the e-mail address is tohave the merchant input the address. Again, this approach is subject toproblems. The merchant must process the transaction and obtain thee-mail address, leading to errors in the spelling of the consumer'semail username and domain. In this way, current systems to input emailaddresses in a retail environment are inefficient and prone to error.Current applications and systems do not provide a way to minimize thetransaction time for acquiring (e.g., capturing or entering through aninterface) the consumer's email address and the related domain name.Poorly designed applications and systems used to capture consumerinformation (e.g., email username and domain) do nothing to decrease thetransaction time that results in a retail environment. Errors and delaysin capturing (e.g., entering) consumer's email username and domain nameresult from poorly designed applications, cumbersome placement of datacapture devices (e.g., inadequate user interfaces, small screen,keyboard) used by the merchants in a retail environment.

BRIEF SUMMARY

The suggestor system provides an effective and efficient way to capturea user identifier (e.g., for a computer system), such as an emailaddress of a consumer in a retail environment.

In one example embodiment, a method for generating a user identifier isprovided. The method receives input data, generates one or moresuggested first text portions based on the input data, outputs at leastone of the suggested first text portions, and receives an indication ofa selection of a selected first text portion. In this embodiment, themethod further generates, by a processor, one or more suggested secondtext portions based on the input data, outputs at least one of thesuggested second text portions, receives an indication of a selectedsecond text portion, and generates the user identifier based on theselected first text portion and the selected second text portion.

In one embodiment, the selected first portion comprises a username, theselected second portion comprises a domain name, and the user identifiercomprises an email address. In another embodiment, generating the one ormore suggested first text portions includes receiving additional inputdata, and updating the one or more suggested first text portions basedon the additional input data. In this embodiment, the additional inputdata may comprise one or more letters of a text portion. In anotherembodiment, generating the one or more suggested second text portionsincludes receiving additional input data, and updating the one or moresuggested second text portions based on the additional input data. Inthis embodiment, the additional input data may comprise one or moreletters of a text portion. In another embodiment, the one or moresuggested second text portions are further based on the indication of aselection of a first text portion. In a further embodiment, outputtingat least one of the suggested first text portions includes calculating aprobability of selection of each suggested first text portion,generating a rank-ordered list of at least one of the suggested firsttext portions, the list ranked by probability of selection, andoutputting the rank-ordered list. In yet another embodiment, outputtingthe second text portions includes calculating a probability of selectionof each suggested second text portion, generating a rank-ordered list ofat least one of the suggested second text portions, the list ranked byprobability of selection, and outputting the rank-ordered list. Inanother embodiment, the suggested first text portions and the suggestedsecond text portions are generated using one or more statistical models.In this embodiment, the method may further train one or more of thestatistical models by collecting information regarding the one or morestatistical models. In this case, the information may include receivedfeedback, or click-stream information that identifies parts of agraphical user interface a user has clicked on during generation of theuser identifier. In this embodiment, the method may calculateperformance indicators based on the received information, and update oneor more of the statistical models based on the calculated performanceindicators. In some embodiments, the method may store the useridentifier in a memory. In another embodiment, the input data mayinclude a first name, a last name, geographical information (zip code,address, longitude-latitude coordinates), an age, a gender, a type ofcredit card, or a bank.

In another example embodiment, an apparatus for generating a useridentifier is provided that includes at least one processor and at leastone memory including program code, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to receive input data, generate one or moresuggested first text portions based on the input data, output at leastone of the suggested first text portions, and receive an indication of aselection of a selected first text portion. In this embodiment, the atleast one memory and the computer program code are further configuredto, with the at least one processor, cause the apparatus to generate oneor more suggested second text portions based on the input data, outputsat least one of the suggested second text portions, receive anindication of a selected second text portion, and generate the useridentifier based on the selected first text portion and the selectedsecond text portion.

In one embodiment, the selected first portion comprises a username, theselected second portion comprises a domain name, and the user identifiercomprises an email address. In another embodiment, generating the one ormore suggested first text portions includes receiving additional inputdata, and updating the one or more suggested first text portions basedon the additional input data. In this embodiment, the additional inputdata may comprise one or more letters of a text portion. In anotherembodiment, generating the one or more suggested second text portionsincludes receiving additional input data, and updating the one or moresuggested second text portions based on the additional input data. Inthis embodiment, the additional input data may comprise one or moreletters of a text portion. In another embodiment, the one or moresuggested second text portions are further based on the indication of aselection of a first text portion. In a further embodiment, outputtingat least one of the suggested first text portions includes calculating aprobability of selection of each suggested first text portion,generating a rank-ordered list of at least one of the suggested firsttext portions, the list ranked by probability of selection, andoutputting the rank-ordered list. In yet another embodiment, outputtingthe second text portions includes calculating a probability of selectionof each suggested second text portion, generating a rank-ordered list ofat least one of the suggested second text portions, the list ranked byprobability of selection, and outputting the rank-ordered list. Inanother embodiment, the suggested first text portions and the suggestedsecond text portions are generated using one or more statistical models.In this embodiment, the at least one memory and the computer programcode are further configured to, with the at least one processor, causethe apparatus to train one or more of the statistical models bycollecting information regarding the one or more statistical models. Inthis case, the information may include received feedback, orclick-stream information that identifies parts of a graphical userinterface a user has clicked on during generation of the useridentifier. In this embodiment, the method may calculate performanceindicators based on the received information, and update one or more ofthe statistical models based on the calculated performance indicators.In another embodiment, the selected first portion comprises a username,the selected second portion comprises a domain name, and the useridentifier comprises an email address. In some embodiments, the at leastone memory and the computer program code are further configured to, withthe at least one processor, cause the apparatus to store the useridentifier in a memory. In another embodiment, the input data mayinclude a first name, a last name, geographical information (zip code,address, longitude-latitude coordinates), an age, a gender, a type ofcredit card, or a bank.

In another example embodiment, a computer program product for generatinga user identifier is provided that includes at least one non-transitorycomputer-readable storage medium having computer-executable codeportions stored therein, the computer-executable code portionscomprising program code instructions that, when executed, cause anapparatus to receive input data, generate one or more suggested firsttext portions based on the input data, output at least one of thesuggested first text portions, and receive an indication of a selectionof a selected first text portion. In this embodiment, the program codeinstructions, when executed, cause the apparatus to generate one or moresuggested second text portions based on the input data, outputs at leastone of the suggested second text portions, receive an indication of aselected second text portion, and generate the user identifier based onthe selected first text portion and the selected second text portion.

In one embodiment, the selected first portion comprises a username, theselected second portion comprises a domain name, and the user identifiercomprises an email address. In another embodiment, generating the one ormore suggested first text portions includes receiving additional inputdata, and updating the one or more suggested first text portions basedon the additional input data. In this embodiment, the additional inputdata may comprise one or more letters of a text portion. In anotherembodiment, generating the one or more suggested second text portionsincludes receiving additional input data, and updating the one or moresuggested second text portions based on the additional input data. Inthis embodiment, the additional input data may comprise one or moreletters of a text portion. In another embodiment, the one or moresuggested second text portions are further based on the indication of aselection of a first text portion. In a further embodiment, outputtingat least one of the suggested first text portions includes calculating aprobability of selection of each suggested first text portion,generating a rank-ordered list of at least one of the suggested firsttext portions, the list ranked by probability of selection, andoutputting the rank-ordered list. In yet another embodiment, outputtingthe second text portions includes calculating a probability of selectionof each suggested second text portion, generating a rank-ordered list ofat least one of the suggested second text portions, the list ranked byprobability of selection, and outputting the rank-ordered list. Inanother embodiment, the suggested first text portions and the suggestedsecond text portions are generated using one or more statistical models.In this embodiment, the program code instructions, when executed, causethe apparatus to train one or more of the statistical models bycollecting information regarding the one or more statistical models. Inthis case, the information may include received feedback, orclick-stream information that identifies parts of a graphical userinterface a user has clicked on during generation of the useridentifier. In this embodiment, the method may calculate performanceindicators based on the received information, and update one or more ofthe statistical models based on the calculated performance indicators.In some embodiments, the program code instructions, when executed, causethe apparatus to store the user identifier in a memory. In anotherembodiment, the input data may include a first name, a last name,geographical information (zip code, address, longitude-latitudecoordinates), an age, a gender, a type of credit card, or a bank.

In another example embodiment, an apparatus for generating a useridentifier is provided including means for receiving input data,generating one or more suggested first text portions based on the inputdata, outputting at least one of the suggested first text portions, andreceiving an indication of a selection of a selected first text portion.In this embodiment, the apparatus further includes means for generatingone or more suggested second text portions based on the input data,outputting at least one of the suggested second text portions, receivingan indication of a selected second text portion, and generating the useridentifier based on the selected first text portion and the selectedsecond text portion.

Other systems, methods, and features will be, or will become, apparentto one with skill in the art upon examination of the following figuresand detailed description. It is intended that all such additionalsystems, methods, features and be included within this description, bewithin the scope of the disclosure, and be protected by the followingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The email suggestor system may be better understood with reference tothe following drawings and description. Non-limiting and non-exhaustivedescriptions are described with reference to the following drawings. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating principles. In the figures, likereferenced numerals may refer to like parts throughout the differentfigures unless otherwise specified.

FIG. 1 shows components of the email suggestor system configuration.

FIG. 2 shows an email suggestor system configuration.

FIG. 3 shows a logic flow of the email suggestor system.

FIG. 4 shows a logic flow of an email username model used by the emailsuggestor system.

FIG. 5 shows a logic flow of a domain name model used by the emailsuggestor system.

FIG. 6 shows a series of user interface displays of generated suggestedemail usernames, domain names, and user inputs and selections.

FIG. 7 shows inputs and outputs of the email usernames model and domainnames model.

DETAILED DESCRIPTION

The principles described herein may be embodied in many different forms.Not all of the depicted components may be required, however, and someimplementations may include additional, different, or fewer components.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein. Additional, different or fewer components may be provided.

The email suggestor system collects information through merchantinteractions with consumers, and uses the collected information toreduce the transaction time for acquiring e-mail information from thesame consumer and/or different consumers during subsequent interactionsbetween the merchant and consumers.

FIG. 1 shows components of the email suggestor system configuration 100that includes an email suggestor system 102. The email suggestor system102 may include one or more models to generate suggested emailaddresses. As shown in FIG. 1, email suggestor system includes emailusername model 104 and domain name model 114. Email username model 104includes email username generator 106 configured to generate a list ofemail usernames 108. The domain name model 114 includes a domain namegenerator 116 configured to generate suggested email domain names 118.Though illustrated in FIG. 1 as a single email username model 104 anddomain name model 114, the email suggestor system 102 may includemultiple email username models 104 and multiple domain name models 114.

The email suggestor system 102 may generate one or more useridentifiers, such as a single suggested email address (such as a singleemail username followed by the character “@” followed by a domain name)or multiple suggested email addresses. Additionally or alternatively,the user identifier may be a personal identifier. The email suggestorsystem may also generate a suggested user identifier (e.g., for acomputer system) by receiving input data (e.g., user identity data),generating a suggested first portion of the user identifier based on theinput data, and generating a suggested second portion of the useridentifier based on the input data. The email suggestor system mayprovide the first and second portions of the user identifier to obtainfeedback from a user.

When generating multiple suggested email addresses, the email suggestorsystem 102 may include functionality to rank the multiple suggestedemail addresses, such as ranker 110 and ranker 120. For example, aranker 110 is configured to order the list of email usernames 108 (e.g.,ranked according to belief email usernames 112) according to theprobability of selection of each email username as determined by theranker 110. Likewise, ranker 120 is configured to order the list ofdomain names 118 (e.g., ranked domain names 122) according to theprobability of selection of each domain name.

The domain name model 114 may differ in at least one aspect from theemail username model 104. As discussed in more detail below, one, some,or all of the following may differ: inputs to the models; logic used forthe models (such as the logic used in the email username generator 106and logic used in the domain name generator); and outputs from themodels.

The email username generator 106 and domain name generator 116 may bebased on feedback models and may use data from a variety of sources. Forexample, as illustrated in FIG. 1, the email username generator 106 andthe domain name generator 116 use data collected during interaction(124, 126, 128, 130) with the email suggestor system 102, and datareceived via the data collection system 132, the consumer identity data134, the email usernames and domain names databases 136, or anycombination thereof. As another example, the email username generator106 and domain name generator may receive consumer identity data, asdiscussed in more detail below. Examples of consumer identity datainclude first name, last name, geographical information (zip code,address, longitude-latitude coordinates), age, gender, type of creditcard, bank, as well as real-time feedback of an interacting agent (e.g.,end-user) including letter(s) identifying a consumer's identity inputusing an input device. The consumer identity data may be received and/orinput in a variety of ways, including using different data collectiondevices and systems, and devices used by end-users to input text (suchas by using a keyboard), to input encoded data (such as from a deviceconfigured to read a credit card magnetic stripe), and to input voice(such as by using audio recognition devices).

The interactions (124, 126, 128, 130) include user input of additionaldata (e.g., one or more letters of an email username or selection of asuggested email username 138, and/or letter or letters of a domain nameor selection of a suggested email address 140). Alternatively or inaddition, the interactions (124, 126, 128, 130) include consumer inputof additional data. Although these interactions are described generally,it will be readily apparent that the mechanics of each interaction ismore specific. For example, when receiving a selection, the actual datareceived by the email username generator 106 or the domain namegenerator 116 may only include an indication of a selection (e.g., oneor more bits of data) transmitted by a user. When interpreted, theindication enables the email username generator 106 or the domain namegenerator 116 to identify and select a corresponding suggestion.

One or more persons and/or systems may interact with the email suggestorsystem configuration 100. For example, FIG. 1 illustrates consumers(142, 144), merchants (146, 148), and users 150. The users 150 of theemail suggestor system 102 may include the merchants (146, 148) andrepresentatives and agents (e.g., employees) of the merchants), andothers interacting with the consumer to capture the consumers' emailaddresses (e.g., email username and domain name combination). Merchants,users, customers, and consumers may interact with the email suggestorsystem as end-users. In addition, merchant and user, as used in theexamples throughout, may include a person, organization, application, orsystem(s), or any combination thereof that may use the email suggestorsystem to capture another person's or entity's email address. Themerchants (146, 148) may be affiliated with each other through the useof the email suggestor system 102 so that the consumer identity data 134and email usernames and domain names databases 136 of the respectivemerchants (146, 148) and the email suggestor system 102, respectively,may be accessed through a network 152 to increase the amount ofinformation available and improve the accuracy of the email usernamemodel 104 and the domain name model 114 used to suggest the consumers'email address. The consumer identity data 134 may include real-timedata, as well as a database of past consumer identity data.

The email username generator 106 receives the consumer identity data. Inresponse to receiving the consumer identity data, the email usernamegenerator 106 generates an initial list of suggested email usernames.The list of suggested email usernames are ranked according to aprobability that the suggested email usernames will be selected, and therank-ordered list of suggested email usernames is provided to the user.

Generating the suggested email usernames may be an iterative process.The iteration may be triggered in one of several ways. As one example,the user may provide feedback, such as by entering additional input(e.g., one or more letters of the email username) or by selecting onefrom the ordered list of suggested email usernames. In response toproviding the feedback, the email username generator 106 and/or thedomain name generator may modify its operation in one or more aspects.For example, when the user provides additional input, the email usernamegenerator 106 is configured to generate an updated list of suggestedemail usernames, which is rank-ordered and provided to the user forselection or additional input. When the user selects an email usernamefrom the list of suggested email usernames, the selection is provided tothe user and the email username is received by the domain namegenerator.

The domain name generator is configured to receive one or more inputs,such as the consumer identity data and/or the email username selectionfrom the email user name generator. The domain name generator generatesan initial list of suggested domain names. The ranker ranks the list ofsuggested domain names according to a probability that the domain namewill be selected, and the ordered list of suggested domain names areprovided to the user. The user may enter additional input (e.g., one ormore letters of the domain name) or select from the ordered list ofsuggested domain names. When the user provides additional input, thedomain name generator generates and updated list of suggested domainnames, and the list of suggested domain names are rank-ordered andprovided to the user for selection or additional input. When the userselects a domain name from the list of suggested domain names, theselection is provided to the user and the consumer identity data isupdated to include the email address (e.g., the selected email usernameand domain name combination).

The email suggestor system 102 updates weights for each possible outcome(e.g., trains the models) in the search space for email username anddomain name generation, in order to reduce the likelihood that thesearches for suggested email usernames and domain names include possiblesuggestions that have not been observed over the data. The emailsuggestor system 102 updates the statistical models for email usernamesand domain names generation iteratively, using feedback that may beexplicit (e.g., relevance feedback), or implicit (e.g., click-streaminformation that results from user interaction with the email suggestorsystem 102). The email suggestor system 102 iteratively evaluates theperformance of the statistical models using performance measures.

FIG. 2 shows an email suggestor system configuration 200 that includesthe email suggestor system 102. The email suggestor system 102 includesa processor 202, a memory 206, and a communications interface 204through which the email suggestor system 102 is accessible through thenetwork 152. The memory 206 includes received consumer identity data 208that may include the first name 210 and last name 212 of a consumer(142, 144) and any associated phone number 214, age and socioeconomicdata 216, and/or credit card information 218. The email suggestor system102 may also use geographical information (GIS data) 220 to assist ingenerating the suggested email usernames 222 and suggested domain names224. The memory 206 also includes suggested email usernames 222 andcorresponding probabilities 226 for each email username generated by theemail username generator (email username model 228). The memory 206 alsostores suggested domain names 224 and corresponding probabilities 230for each domain name generated by the domain name generator (domain namemodel 232). The email suggestor system 102 generates suggested emailusername and domain name combinations 234, and correspondingprobabilities for each email username and domain name combination (236,238, 240). The email username generator and the domain name generatormay each employ the same or different ranker (110, 120) to order therespective suggested lists according to the probability rankings 260 ofeach suggested email username and suggested domain name. The emailsuggestor system 102 may use email usernames logic and rules 242, anddomain names logic and rules 244 to generate email usernames and domainnames, respectively. The email suggestor system 102 trains (e.g., update246) the models using performance measurement logic 248 to evaluateperformance indicators 250, and feedback 252 (e.g., user interactionssuch as click-streams 254, and user selections 256 of suggested emailnames and domain names).

FIG. 3 shows a logic flow 300 of the email suggestor system 102. Theemail suggestor system 102 includes logic instructions to receiveconsumer identity data (302). The consumer identity data may include afirst name and a last name for the consumer, and/or additionalinformation collected from various sources. The email suggestor systemmay initially compare the consumer identity data with historicalinformation (e.g., posted and explicit information) to identify theemail address of the consumer. When the email suggestor systemdetermines that the historical information identifies the consumeridentity with a probability of accuracy greater than a configurablethreshold (304), the email suggestor system provides a suggested emailaddress to the user for the consumer (306). The email username generatorgenerates a list of suggested email usernames based on the consumeridentity data (308). The domain name generator generates a list ofsuggested domain names based on the consumer identity data and theinputs from the email username generator (310). The domain namegenerator generates the suggested email domain differently in at leastone aspect from the way the email username generator generates thesuggested email username. The email suggestor system provides a combinedsuggested list of email username and email domain names for userselection, and/or a list of suggested email usernames, and/or a list ofsuggested domain names (312). The email suggestor system uses additionalinput from the user to generate an updated list of email usernames, anupdated list of domain names and/or an updated list of combinations ofemail usernames and domain names. These operations may iterate until auser selection is received, selecting one of the suggested usernames,suggested domain names, and/or suggested email addresses (e.g., an emailusername and domain name combination). When the user selects one ofthese suggested email usernames, the domain name generator uses theselected email username (among other inputs) to generate a list ofdomain names. When the user selects one of the email addresses (e.g., anemail username and domain name combination), the consumer identity datais updated to include the address. The email suggestor system evaluatesthe performance of the email username generator and the domain namegenerator with feedback from the user (e.g., implicit feedback obtainedfrom click-streams that identify user interactions with the emailsuggestor system, and explicit feedback) (314). The email suggestorsystem then updates the email usernames model(s) and domain namesmodel(s) based on the feedback (316).

The email username generator generates the suggested email username andcalculates a probability value for the suggested email username based onthe consumer identity data, geographical information (GIS data) aboutthe consumer identified by the consumer identity data, geographicalinformation about the merchant (e.g., user) interacting with theconsumer, or a suggested email domain, or any combination thereof. Thedomain name generator generates each suggested domain name andcalculates a probability value for the suggested domain name.

The email suggestor system may determine, from multiple statisticalmodels, which of the multiple statistical models to use as the emailusername model, the email domain name model, or both using performancemeasurements to evaluate each model. Although the email suggestor systemmay be configured to use the model(s) observed to have the bestperformance measurements, alternatively the email suggestor system mayselect one model from the several statistical models available, mayselect multiple models from the several statistical models available,and/or may aggregate multiple models. For example, the email suggestorsystem may aggregate multiple statistical models, using for example,information fusion strategies such as borda-count, reciprocal-rank,rank-mixer, rank-booster, among other ranking mixer algorithms. Theemail suggestor system uses relevance feedback information, orclick-stream information that identifies parts of a graphical userinterface (GUI) the user clicks on when receiving the consumer identitydata, or when receiving the user's selections. The email suggestorsystem may calculate, for each of the multiple statistical models usingperformance measures, a performance indicator that indicates theperformance of the respective statistical models. The email suggestorsystem may in turn evaluate the performance indicator of each of themultiple statistical models to determine which of the multiplestatistical models to use as the email username model, the email domainnames model, or both.

FIG. 6 shows a series of user interface displays 600 of generatedsuggested email usernames (602, 604, 606), suggested domain names (608,610, 612), and user inputs (614, 616, 618, 620) and user selection(622). The email suggestor system 102 may generate an initial list ofsuggested email usernames 602 (e.g., based on external input data suchas consumer identity data from a credit card swipe and/or reading thefirst name and last name of the customer). The ranker 110 ranks theemail usernames 602 according to a probability of selection of eachsuggested email username 602. When the email suggestor system 102receives user input 614 (e.g., real-time user feedback) the emailusername generator 106 recalculates the suggested email usernames 604.When the user selects one of the suggested email usernames 606, as theemail username 616 (118), the email suggestor system 102 mayautomatically add the ‘@’ to the end of the email username 618, and thedomain name generator 122 generates a list of the most probable emaildomain names 608 ranked according to a probability of selection of eachsuggested domain name. When the email suggestor system 102 receives userinput 620 (e.g., real-time user feedback, beginning the spelling of thedomain name), the domain name generator 122 uses the user input torecalculate the suggested domain names 610. These operations may iterateuntil the user selects one of the suggested domain names 612, with theemail address 622 being captured by the email suggestor system 102.

The email suggestor system 102 may use ranking logic to generate orderedlists of email usernames and domain names ranked by the probability ofselection, based on historical information (e.g., posted and explicitinformation), and the combination of first name and last name. Forexample, given a first name John and last name Doe, the combination ofemail usernames may include jdoe, johndoe, john, or doe, each with aprobability of selection generated by the email suggestor system logic.Similarly, the email suggestor logic may generate the probability ofselection of each domain name. In one embodiment, the email suggestorsystem logic may generate a list of suggested email addresses(combinations of email usernames and domain names) by searching one ormore email username and domain name databases using the first name, lastname, GIS data, the list of generated email usernames, and the generatedlist of domain names. In this embodiment, the email suggestor systemlogic generates the list of suggested email addresses ranked in theorder of the probability of selection.

The email suggestor system logic may use the first name, last name andgeographical information (GIS) data as inputs, implicit feedback (e.g.,user click streams) and explicit feedback (e.g., a user's previousselection of suggested combinations of email usernames and domainnames). The implicit feedback and explicit feedback may be obtained as aresult of previous user interactions with the email suggestor system togenerate a ranked list of suggested email addresses (e.g., suggestedcombination of email username and domain names) for user selection. Thegeographical information (GIS) data may include store level information(e.g., coordinates and identifies information about the business in thatlocation). The email suggestor system output includes a ranked list ofsuggested usernames followed by domains. The email suggestor system mayuse email username generation logic to generate a list of emailusernames based on the first name, last name and GIS data from a searchof one or more email username and/or domain name databases. The emailsuggestor system and/or user email generation logic may use one or morestatistical models applied to the list of email usernames to determinethe probability of selection (e.g., the likelihood that the suggestedemail username matches the consumer's actual email username) The emailsuggestor system logic may use the GIS data, as well as otherinformation about the location of the consumer, the merchant, and theinteraction between the consumer and merchant, to generate a list ofemail domain names using, for example, approximations based on the GISdata.

The email suggestor system uses performance measurement logic toevaluate and update (e.g., train the one or more statistical models usedto generate the suggested email usernames and domain names) theprobabilities of selection (e.g., weights) to improve the accuracy ofthe suggested email usernames and domain names. The email suggestorsystem may update the one or more statistical models for emailgeneration iteratively using the feedback from the user's interactions(e.g., explicit (relevance feedback) or implicit (e.g., click-streaminformation), or both). The email suggestor system may iterativelyevaluate the performance of the generated statistical models usingperformance measures (e.g., Word Error Rate, Perplexity, ArtificialLattices (recognition lattices), Clustered backoff models).

FIG. 4 shows a logic flow of an email username model used by the emailsuggestor system. The email username generator receives the consumeridentity data 302. The email username generator generates an initiallist of suggested email usernames (402). The list of suggested emailusernames are ranked according to a probability that the email usernameis selected (404), and the ordered list of suggested email usernames areprovided to the user (406). The user may enter additional input (e.g.,one or more letters of the email username) or select from the orderedlist of suggested email usernames. When the user provides additionalinput, the email username generator generates and updated list ofsuggested email usernames, and list of suggested email usernames arerank-ordered and provided to the user for selection or additional input(408). When the user selects an email username from the list ofsuggested email usernames, the selection is provided to the user and theemail username is received by the domain name generator (410).

FIG. 5 shows a logic flow of a domain name model used by the emailsuggestor system. The domain name generator receives the consumeridentity data and/or the email username selection from the emailusername generator (302). The domain name generator generates an initiallist of suggested domain names (502). The list of suggested domain namesare ranked according to a probability that the domain name is selected(504), and the ordered list of suggested domain names are provided tothe user (506). The user may enter additional input (e.g., one or moreletters of the domain name) or select from the ordered list of suggesteddomain names. When the user provides additional input, the domain namegenerator generates and updated list of suggested domain names, and thelist of suggested domain names are rank-ordered and provided to the userfor selection or additional input (508). When the user selects a domainname from the list of suggested domain names, the selection is providedto the user and the consumer identity data is updated to include theemail address (e.g., a string including the selected email username andselected domain name) (510).

FIG. 7 shows inputs and outputs 700 of the email usernames model 702 anddomain names model 704, and user interactions (e.g., includingfeedback). The email suggestor system 102 may use various data inputsincluding consumer identity data such as credit card 706, socialeconomic 708, age 710, and user input 712, as well as GIS data 714. Theemail username logic (e.g., email usernames model 702), and the domainname logic (e.g., the domain name model 704) may both receive thevarious data inputs (706, 708, 710, 712, 714). The email username logicmay also receive suggested domain names 716 from the domain name logicto assist in identifying suggested email usernames 718. The emailusername logic 702 generates suggested email usernames 718 that thedomain name logic 704 uses to identify suggested domain names 720. Whenthe user selects a suggested email username 718, the domain name logic704 uses the email username selection (e.g., feedback 722, 724) toidentify suggested email usernames and domain names combinations (e.g,email address 726). The email username logic and domain name logic aretrained using feedback (722, 724) (e.g., implicit feedback such asclick-streams, and explicit feedback such as user selections) to improvethe accuracy of the suggested email usernames 718 and suggested domainnames 720, and to therefore increase the speed with which emailaddresses 726 are selected.

The email suggestor system provides a way to acquire a user's emailaddress in a retail space with minimum transaction time. The emailsuggestor system provides an efficient and effective way for merchantsto collect and consumers to provide email addresses (e.g., email addressand domain name combinations). The email suggestor system usesinformation about the consumer to accurately forecast and predict theconsumer's email address and domain name, including information capturedfrom a credit card number, the consumers' Groupon® profile and/or otherconsumer identity data. The email suggestor system reduces the amount oftime the merchant spends with the consumer to accurately determine theconsumer's email username and domain name, even given complicated oruncommon spellings of the consumer's first name and last name Generally,the email suggestor system allows the user (e.g., a merchant) to acquirevaluable contact information about another person (e.g., consumer) in aneffective and efficient way that reduces the transaction time to do so.

The email suggestor system uses the information captured from themagnetic strip of a consumer's credit card, including the first and lastname of the consumer to generate permutations of email username anddomain name combinations possible for the consumer. The email suggestorsystem may use information available about the consumer's name and oridentity in order to assist in determining the email username and domainname for the consumer. The email suggestor system may provide suggestedemail addresses during the process of capturing (e.g., acquiring and orentering) the consumer's contact and identity information.

In addition to the consumer contact and identity information that may beacquired from the magnetic strip from a consumer's credit card, aconsumer may provide such information during a verbal conversation witha merchant. For example, when a consumer contacts a merchant by phone toplace an order or reservation, the email suggestor system performs areverse phone number lookup during the conversation to identify theconsumer's name so that when the merchant requests the email address ofthe consumer during the call or sometime thereafter, the email suggestorsystem provides an ordered list of suggested email usernames andsuggested domain names and assist in determining the accurate spellingof the consumer's email address. When the email suggestor systemacquires a name for a consumer, the email suggestor system can forecastthe email address of the consumer. In addition, whenever the emailsuggestor system acquires or possesses information about a consumer'sidentity, the email suggestor system forecasts the consumer emailaddress and intelligent selection of the domain name.

The email suggestor system may assist a user in inputting the emailaddress into a contact book (e.g., a database). For example, a user mayenter the first and last name of a contact in a contact book, and beforeentering the email address of the contact, the email suggestor systemmay forecast the email address of the contact and suggests the emailaddressed to the user to expedite and minimize the transaction time tocomplete the contact information in the contact book.

The email suggestor system interfaces with contact, order, and paymentapplications and/or systems (e.g., e-commerce applications and systems)used by the merchant in order to reduce the transaction time to acquirean accurate email address for the consumer.

Identifying the username (e.g., email username) for a given domain mayfollow a format and/or rules established by the domain provider. Forexample, an organization (e.g., commercial business, school, and theUniversity) may impose a format on the username such as first initialcombined with last name (e.g., FLastname@companyABCXYZ.com).Accordingly, the email suggestor system determines whether the domainfor the email username uses particular rules or formatting to moreaccurately forecast and or suggest to the merchant the user's emailaddress given other consumer identity information. The email suggestorsystem may not know or possess the domain name for the consumer's emailaddress before entering consumer identity information such as the firstname or last name of the consumer. The email suggestor system accessesthe variety of domains used by consumers that patronize the merchantusing the email suggestor system, and uses the formatting and/or rules,and restrictions and/or prohibitions (e.g., valid email usernames maynot include an ‘!’.) imposed by the respective domains to accuratelydetermine the consumers' domain name and/or email username.

The email suggestor system may implement one or more rules to determineand/or forecast the consumer's email address. For example, the model mayinclude rules that use location information, such as the zip code,proximity to a predetermined location (e.g., a college campus, acorporate campus, etc.), and/or other geographical information (such asGIS data) to determine the suggested username and/or domain name. Morespecifically, the location information may be used in conjunction withother information to generate the suggested username and/or domain name.

As another example, the email suggestor system may include rules thatuse various inputs, such as information that the customer is in a coffeeshop within a geographically predetermined distance from a school, todecide whether to use the geographical model to forecast the probabilitythat the domain name for the user's email address is the domain name ofthe school. In this way, the email suggestor system may improve theforecast of the consumer's email username given the formatting and/orrules used for the school's domain.

Likewise, the email suggestor system may include a hierarchy of rules,in which the result of a first set of rules may trigger the use of asecond set of rules. For example, a determination of the domain name maydictate the rules and/or formatting for the email username of theconsumer. More specifically, in the event that the rules of the emailsuggestor system determine within a predetermined probability that thedomain is a university domain, the email suggestor system may then use asecond set of rules to generate the suggested usernames (e.g., using thetemplate for usernames for the university).

As discussed above, the email suggestor system may receive informationfrom various sources. For example, the email suggestor system mayreceive the name (first and last), geographical information, and otherdata about the identity of the consumer and the merchant. The emailsuggestor system may likewise use information the merchant may alreadypossess about consumers who have previously purchased from the merchantand/or consumers who have visited the merchant's website (e.g.,information captured from cookies and foreign cookies in particular)and/or information acquired by the merchant through other interactionswith the merchant. The merchant may have the consumer's contactinformation and email address and location information for thoseconsumers who have previously interacted (e.g., made a purchase and/ormade inquiry) with the merchant and/or affiliate merchants who use theemail suggestor system. When the consumer has previously interacted withthe merchant and/or affiliate merchants who use the email suggestorsystem, the email suggestor system identifies a one-to-one match to theconsumer's previously captured identity information to identify theemail address of the consumer. For example, when the email suggestorsystem identifies the credit card as belonging to a consumer who haspreviously interacted with the merchant and an affiliate merchant whoalso uses the email suggestor system, the email suggestor systemidentifies the one-to-one match to the consumer's email address andlocation information (e.g., geographical). Accordingly, when the emailsuggestor system identifies an existing profile for the consumer, wherethe profile identifies the name of the consumer and the consumer'scredit card numbers, the email suggestor system can identify theconsumers email address immediately.

The email suggestor system may also use one or more models to forecastand/or pre-compute the lists of suggested email usernames and domainnames for the email suggestor system user (e.g., merchant) to select inorder to minimize the transaction time needed to acquire the accurateemail address of the consumer. The email suggestor system also uses themerchant selection to train the one or more models used to suggest theemail usernames and domain names permutations. The suggested lists ofemail usernames and domain names may be ranked based on configurablerules. For example, multiple models may be employed to work in parallelto forecast the user's email address and domain name permutations, andthe email suggestor system may rank the permutations according to apriority ranking of the multiple models used. For example, the emailsuggestor system may prioritize and/or rank multiple models and theforecasts generated by the respective models, where one model may betrained using geographical information about the consumer, while anothermodel may be trained using the age and social economic information aboutthe consumer, while even another model may be trained using informationabout the merchant in combination with the consumer's information.Depending on configurable rules, the ranking of the forecasts generatedby the models may determine the sequence that suggested email usernamesand domain names are listed for selection. The email suggestor systemmay also use a national model (e.g., trained using an aggregation ofnationally captured email usernames and domains names information), alocal model (e.g., trained using a subset of captured email usernamesand domains names information within a configurable geographical areaand/or location), and/or a hyper-local model (e.g., trained using asubset of the subset of captured email usernames and domains namesinformation within a configurable geographical area and/or location)trained to each identify methods to accurately predict the consumer'semail address and domain name.

Using performance metrics and key indicators the models are trained andthe email suggestor system may prioritize the models based on theperformance metrics and key indicators. The email suggestor system usesconfigurable sample populations of email usernames and domain names fromvarious sources including the merchants that use the email suggestorsystem. For example, the email suggestor system determines theprobability that the combination of the first three letters of aconsumer's email address, and the first and last name from theconsumer's credit card results in an accurate email address and domainname. The email suggestor system captures the selection made by themerchant regarding the email username and domain name of the consumer inorder to refine and train the model used to accurately predict the emailuser's name and domain name.

The email suggestor system may use performance measures and keyperformance indicators to identify the accuracy of the forecasts of themodels.

The email suggestor system identifies the email usernames and domainnames the merchant has previously captured and/or stored for use. Forexample, the email suggestor system may determine that for a coffee shopmerchant in geographical proximity to a university with low demographicdiversity (e.g., a small town university in a geographically rural areaand/or with low commercial diversity), there is a high probability thatthe email of a consumer uses a domain name from the University. Incontrast, the email suggestor system may determine that for anothercoffee shop merchant in geographical proximity to a big city universitywith high demographic diversity (e.g., a university in a geographicallybig city and/or with high commercial diversity), there is a lowprobability that the email of the consumer uses a domain name from thebig city university.

The email suggestor system provides an effective and efficient way toreceive the captured customer identity information in various sequencesor orders of delivery and/or receipt. For example, the email suggestorsystem may first receive, through a credit card swipe, the first andlast name of the consumer, then receives character inputs entered by themerchant to identify the consumer's email username, and subsequentlyand/or in parallel determines the domain name for the email addressgiven all or some portion of the consumer's email username. The emailsuggestor system may use the domain name to infer the structure and/orformat of the consumer's email username, when the email suggestor systemhas aggregate information about the probability of the domain being usedat the merchant's store. Moreover, the email suggestor system trains themodels to detect changes in demographics for the merchants so that thepriority ranking and use of one or more models changes responsive tochanges in the demographics. For example, the consumers that interactwith a merchant may correlate to seasonal factors (e.g., certainconsumers frequent a particular merchant only during particular seasonsand/or times of the year), and the email suggestor system adaptsresponsibly to such correlations to seasonal factors in order toaccurately forecast the domain name to suggest. The email suggestorsystem identifies trends and changes in consumer demographics for themerchants and adapts the models accordingly to accurately forecast theconsumer's email username and domain name.

The email suggestor system provides an effective and efficient way toenhance and improve the transaction time for consumer-facing operationsin a retail environment. For example, when the email suggestor systemcaptures a consumer's credit card information as a result of swiping thecredit card and/or using near field technology to capture theinformation when the credit card is in proximity to the payment system,the email suggestor system may assist the merchant in offering rewardsto the consumer by minimizing the transaction time used to capture theemail address of the consumer.

The email suggestor system also may be used by the user (e.g., merchant)in transactions with the consumer that do that involve payment. Forexample, in a checkout interaction between the consumer and the merchantthat does not involve payment, where the consumer's payment informationis already on file with the merchant, the merchant may use the emailsuggestor system to generate a form that accurately includes theconsumer's identity information including the email address and domainname so that the consumer may merely add a response requested by themerchant (e.g., how did you like our service?), rather than the consumerhaving to provide the identity information including the email usernameand domain name. The email suggestor system may be used by a merchantwherever the merchant is collecting identity information to assist inminimizing the transaction time to complete the capture of theconsumer's email username and domain name.

The email suggestor system provides merchants a way to efficiently andeffectively capture email username and domain name information. Consideran example where a consumer calls (e.g., during a face-to-face verbaldata capture transaction) to rent a car from a merchant. The merchantmay ask whether the consumer would like an email receipt for the carrental so that the consumer may retrieve the receipt from the consumer'smobile device. Currently, the consumer must spell out the consumer'semail username and domain names to the merchant. However, using theemail suggestor system, the merchant is able to suggest the emailusername and domain name to the consumer based on the first few lettersof the consumer's name (e.g., in particular reducing the transactiontime for complicated consumer names and/or uncommonly used names,including complex and uncommonly used domain names).

The system 102 may be deployed as a general computer system used in anetworked deployment. The computer system may operate in the capacity ofa server or as a client user computer in a server-client user networkenvironment, or as a peer computer system in a peer-to-peer (ordistributed) network environment. The computer system may also beimplemented as or incorporated into various devices, such as a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a mobile device, a palmtop computer, a laptop computer,a desktop computer, a communications device, a wireless telephone, aland-line telephone, a control system, a camera, a scanner, a facsimilemachine, a printer, a pager, a personal trusted device, a web appliance,a network router, switch or bridge, or any other machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. In a particular embodiment, thecomputer system may be implemented using electronic devices that providevoice, video or data communication. Further, while a single computersystem may be illustrated, the term “system” shall also be taken toinclude any collection of systems or sub-systems that individually orjointly execute a set, or multiple sets, of instructions to perform oneor more computer functions.

The computer system may include a processor, such as a centralprocessing unit (CPU), a graphics processing unit (GPU), or both. Theprocessor may be a component in a variety of systems. For example, theprocessor may be part of a standard personal computer or a workstation.The processor may be one or more general processors, digital signalprocessors, application specific integrated circuits, field programmablegate arrays, servers, networks, digital circuits, analog circuits,combinations thereof, or other now known or later developed devices foranalyzing and processing data. The processors and memories discussedhere and in the claims below may be embodied in and implemented in oneor multiple physical chips or circuit combinations. The processor mayexecute a software program, such as code generated manually (i.e.,programmed).

The computer system may include a memory that can communicate via a bus.The memory may be a main memory, a static memory, or a dynamic memory.The memory may include, but may not be limited to, computer readablestorage media such as various types of volatile and non-volatile storagemedia, including but not limited to random access memory, read-onlymemory, programmable read-only memory, electrically programmableread-only memory, electrically erasable read-only memory, flash memory,magnetic tape or disk, optical media and the like. In one case, thememory may include a cache or random access memory for the processor.Alternatively or in addition, the memory may be separate from theprocessor, such as a cache memory of a processor, the memory, or othermemory. The memory may be an external storage device or database forstoring data. Examples may include a hard drive, compact disc (“CD”),digital video disc (“DVD”), memory card, memory stick, floppy disc,universal serial bus (“USB”) memory device, or any other deviceoperative to store data. The memory may be operable to storeinstructions executable by the processor. The functions, acts or tasksillustrated in the figures or described herein may be performed by theprogrammed processor executing the instructions stored in the memory.The functions, acts or tasks may be independent of the particular typeof instructions set, storage media, processor or processing strategy andmay be performed by software, hardware, integrated circuits, firm-ware,micro-code and the like, operating alone or in combination. Likewise,processing strategies may include multiprocessing, multitasking,parallel processing and the like.

The computer system may further include a display, such as a liquidcrystal display (LCD), an organic light emitting diode (OLED), a flatpanel display, a solid state display, a cathode ray tube (CRT), aprojector, a printer or other now known or later developed displaydevice for outputting determined information. The display may act as aninterface for the user to see the functioning of the processor, orspecifically as an interface with the software stored in the memory orin the drive unit.

Additionally, the computer system may include an input device configuredto allow a user to interact with any of the components of system. Theinput device may be a number pad, a keyboard, or a cursor controldevice, such as a mouse, or a joystick, touch screen display, remotecontrol or any other device operative to interact with the system.

The computer system may also include a disk or optical drive unit. Thedisk drive unit may include a computer-readable medium in which one ormore sets of instructions, e.g. software, can be embedded. Further, theinstructions may perform one or more of the methods or logic asdescribed herein. The instructions may reside completely, or at leastpartially, within the memory and/or within the processor duringexecution by the computer system. The memory and the processor also mayinclude computer-readable media as discussed above.

The present disclosure contemplates a computer-readable medium thatincludes instructions or receives and executes instructions responsiveto a propagated signal, so that a device connected to a network maycommunicate voice, video, audio, images or any other data over thenetwork. Further, the instructions may be transmitted or received overthe network via a communication interface. The communication interfacemay be a part of the processor or may be a separate component. Thecommunication interface may be created in software or may be a physicalconnection in hardware. The communication interface may be configured toconnect with a network, external media, the display, or any othercomponents in system, or combinations thereof. The connection with thenetwork may be a physical connection, such as a wired Ethernetconnection or may be established wirelessly as discussed below.Likewise, the additional connections with other components of the DCBRsystem 102 may be physical connections or may be established wirelessly.In the case of a service provider server, the service provider servermay communicate with users through the communication interface.

The network may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to TCP/IP based networking protocols.

The computer-readable medium may be a single medium or multiple media,such as a centralized or distributed database, and/or associated cachesand servers that store one or more sets of instructions. The term“computer-readable medium” may also include any medium that may becapable of storing, encoding or carrying a set of instructions forexecution by a processor or that may cause a computer system to performany one or more of the methods or operations disclosed herein.

The computer-readable medium may include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium also may be a randomaccess memory or other volatile re-writable memory. Additionally, thecomputer-readable medium may include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an email or other self-containedinformation archive or set of archives may be considered a distributionmedium that may be a tangible storage medium. The computer-readablemedium is preferably a non-transitory storage medium. Accordingly, thedisclosure may be considered to include any one or more of acomputer-readable medium or a distribution medium and other equivalentsand successor media, in which data or instructions may be stored.

Alternatively or in addition, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, may be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments may broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that may be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system may encompass software, firmware, and hardwareimplementations.

The methods described herein may be implemented by software programsexecutable by a computer system (e.g., a computer program productcomprising a non-transitory computer-readable storage medium). Further,implementations may include distributed processing, component/objectdistributed processing, and parallel processing. Alternatively or inaddition, virtual computer system processing maybe constructed toimplement one or more of the methods or functionality as describedherein.

Although components and functions are described that may be implementedin particular embodiments with reference to particular standards andprotocols, the components and functions are not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, andHTTP) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The illustrations described herein are intended to provide a generalunderstanding of the structure of various embodiments. The illustrationsare not intended to serve as a complete description of all of theelements and features of apparatus, processors, and systems that utilizethe structures or methods described herein. Many other embodiments maybe apparent to those of skill in the art upon reviewing the disclosure.Other embodiments may be utilized and derived from the disclosure, suchthat structural and logical substitutions and changes may be madewithout departing from the scope of the disclosure. Additionally, theillustrations are merely representational and may not be drawn to scale.Certain proportions within the illustrations may be exaggerated, whileother proportions may be minimized. Accordingly, the disclosure and thefigures are to be regarded as illustrative rather than restrictive.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the description. Thus, to the maximumextent allowed by law, the scope is to be determined by the broadestpermissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

1.-39. (canceled)
 40. An email suggestor system for interfacing each oftwo or more affiliate merchant devices including at least a firstmerchant device and a second merchant device, with a payment applicationto reduce a transaction time for consumer-facing operations in a retailenvironment, the email suggestor system comprising an apparatus, theapparatus comprising at least one processor and at least one memoryincluding program code, the at least one memory and the computer programcode configured to, with the at least one processor, cause the apparatusto: receive, from the first merchant device, during a first transaction,a first name and a last name of a consumer and an associated paymentmethod; subsequent to the first transaction, receive, during a secondtransaction, with the second merchant device, identity information;determine that the consumer previously interacted with an affiliatemerchant device, the affiliate merchant device being the first merchantdevice, subsequently match the identity information received during thesecond transaction to the first and last name of the consumer receivedduring the first transaction; identify, during the second transaction atthe second merchant device, a consumer profile associated with thepayment method, the consumer profile associated with the email suggestorsystem; identify, from the consumer profile, an associated email addressassociated with the consumer profile; provide, during the secondtransaction, an interface to a third-party payment applicationconfigured to reduce the transaction time, subsequent to theidentification of the associated email address associated with theconsumer profile; and cause, during the second transaction, display ofthe email address associated with the consumer profile associated withthe payment method.
 41. The email suggestor system according to claim40, wherein the receiving identity information during the secondtransaction comprises: receiving input data associated with the secondtransaction; generating one or more suggested first text portions basedon the input data; outputting at least one of the suggested first textportions; and receiving an indication of a selection of a first textportion.
 42. The email suggestor system according to claim 41, whereinthe receiving identity information during the second transaction furthercomprises: generating one or more suggested second text portions basedon the input data; outputting at least one of the suggested second textportions; receiving an indication of a selection of a second textportion; and generating the user identifier based on the selected firsttext portion and the selected second text portion.
 43. The emailsuggestor system according to claim 40, wherein the receiving identityinformation during the second transaction comprises: receiving, in aninstance in which the consumer has previously transacted with themerchant device or an affiliated merchant device, the first name and thelast name from the merchant device during the second transaction. 44.The email suggestor system according to claim 40, wherein the receivingidentity information during the second transaction comprises: receiving,in an instance in which the consumer has previously visited a websiteassociated with the merchant device or an affiliated merchant device,the first name and the last name from the merchant during the secondtransaction.
 45. The email suggestor system according to claim 40,wherein the receiving identity information during the second transactioncomprises: using near field technology to capture the identityinformation when a credit card is in proximity to the payment system,wherein the suggesting, during the second transaction, of the emailaddress associated with the payment method comprises: identifying anexisting profile for the consumer, where the profile identifies theidentity information of the consumer and the payment method.
 46. Theemail suggestor system according to claim 40, wherein the apparatusfurther comprises computer program code configured to, with theprocessor, cause the apparatus to: wherein the suggesting, during thesecond transaction, of the email address associated with the paymentmethod comprises: identifying an existing profile for the consumer,wherein the existing profile identifies the identity information of theconsumer and the payment method.
 47. The email suggestor systemaccording to claim 40, wherein the apparatus further comprises computerprogram code configured to, with the processor, cause the apparatus to:wherein, in an instance in which the payment information is on file withthe merchant device, generate a form that includes the identityinformation and the email address; and requesting a response from theconsumer.
 48. A email suggestor computer program product for interfacingeach of two or more affiliate merchant devices including at least afirst merchant device and a second merchant device, with a paymentapplication to reduce a transaction time for consumer-facing operationsin a retail environment, the computer program product comprising atleast one non-transitory computer-readable storage medium havingcomputer-executable program code instructions stored therein, thecomputer-executable program code instructions comprising program codeinstructions for: receiving, from the first merchant device, during afirst transaction, a first name and a last name of a consumer and anassociated payment method; subsequent to the first transaction,receiving, during a second transaction, with the second merchant device,identity information; determining that the consumer previouslyinteracted with an affiliate merchant device, the affiliate merchantdevice being the first merchant device, subsequently matching theidentity information received during the second transaction to the firstand last name of the consumer received during the first transaction; andidentifying, during the second transaction at the second merchantdevice, a consumer profile associated with the payment method, theconsumer profile associated with the email suggestor system;identifying, from the consumer profile, an associated email addressassociated with the consumer profile; providing, during the secondtransaction, an interface to a third-party payment applicationconfigured to reduce the transaction time, subsequent to theidentification of the associated email address associated with theconsumer profile; and causing, during the second transaction, display ofthe email address associated with the consumer profile associated withthe payment method.
 49. The computer program product according to claim48, wherein the receiving identity information during the secondtransaction comprises: receiving input data associated with the secondtransaction; generating one or more suggested first text portions basedon the input data; outputting at least one of the suggested first textportions; and receiving an indication of a selection of a first textportion.
 50. The computer program product according to claim 49, whereinthe receiving identity information during the second transaction furthercomprises: generating one or more suggested second text portions basedon the input data; outputting at least one of the suggested second textportions; receiving an indication of a selection of a second textportion; and generating the user identifier based on the selected firsttext portion and the selected second text portion.
 51. The computerprogram product according to claim 48, wherein the receiving identityinformation during the second transaction comprises: receiving, in aninstance in which the consumer has previously transacted with themerchant device or an affiliated merchant device, the first name and thelast name from the merchant device during the second transaction. 52.The computer program product according to claim 48, wherein thereceiving identity information during the second transaction comprises:receiving, in an instance in which the consumer has previously visited awebsite associated with the merchant device or an affiliated merchantdevice, the first name and the last name from the merchant during thesecond transaction.
 53. The computer program product according to claim48, wherein the receiving identity information during the secondtransaction comprises: using near field technology to capture theidentity information when a credit card is in proximity to the paymentsystem, wherein the suggesting, during the second transaction, of theemail address associated with the payment method comprises: identifyingan existing profile for the consumer, where the profile identifies theidentity information of the consumer and the payment method.
 54. Thecomputer program product according to claim 48, wherein the suggesting,during the second transaction, of the email address associated with thepayment method comprises: identifying an existing profile for theconsumer, wherein the existing profile identifies the identityinformation of the consumer and the payment method.
 55. The computerprogram product according to claim 48, wherein the computer-executableprogram code instructions further comprise program code instructionsfor: in an instance in which the payment information is on file with themerchant device, generating a form that includes the identityinformation and the email address; and requesting a response from theconsumer.
 56. A computer-implemented method for programmaticallyinterfacing each of two or more affiliate merchant devices including atleast a first merchant device and a second merchant device, with apayment application to reduce a transaction time for consumer-facingoperations in a retail environment, the method comprising: receiving,from the first merchant device, during a first transaction, a first nameand a last name of a consumer and an associated payment method;subsequent to the first transaction, receiving, during a secondtransaction, with the second merchant device, identity information;determining that the consumer previously interacted with an affiliatemerchant device, the affiliate merchant device being the first merchantdevice, subsequently matching the identity information received duringthe second transaction to the first and last name of the consumerreceived during the first transaction; and identifying, during thesecond transaction, a consumer profile associated with the paymentmethod, the consumer profile associated with the email suggestor system;identify, from the consumer profile, an associated email addressassociated with the consumer profile; providing, during the secondtransaction, an interface to a third-party payment application; andcausing, during the second transaction, display of the email addressassociated with the consumer profile associated with the payment methodconfigured to reduce the transaction time, subsequent to theidentification of the associated email address associated with theconsumer profile.
 57. The computer-implemented method according to claim56, wherein the receiving identity information during the secondtransaction comprises: receiving input data associated with the secondtransaction; generating one or more suggested first text portions basedon the input data; outputting at least one of the suggested first textportions; and receiving an indication of a selection of a first textportion.
 58. The computer-implemented method according to claim 57,wherein the receiving identity information during the second transactionfurther comprises: generating one or more suggested second text portionsbased on the input data; outputting at least one of the suggested secondtext portions; receiving an indication of a selection of a second textportion; and generating the user identifier based on the selected firsttext portion and the selected second text portion.
 59. Thecomputer-implemented method according to claim 56, wherein the receivingidentity information during the second transaction comprises: receiving,in an instance in which the consumer has previously transacted with themerchant device or an affiliated merchant device, the first name and thelast name from the merchant device during the second transaction. 60.The computer-implemented method according to claim 56, wherein thereceiving identity information during the second transaction comprises:receiving, in an instance in which the consumer has previously visited awebsite associated with the merchant device or an affiliated merchantdevice, the first name and the last name from the merchant during thesecond transaction.
 61. The computer-implemented method according toclaim 56, wherein the receiving identity information during the secondtransaction comprises: using near field technology to capture theidentity information when a credit card is in proximity to the paymentsystem, wherein the suggesting, during the second transaction, of theemail address associated with the payment method comprises: identifyingan existing profile for the consumer, where the profile identifies theidentity information of the consumer and the payment method.
 62. Thecomputer-implemented method according to claim 56, wherein thesuggesting, during the second transaction, of the email addressassociated with the payment method comprises: identifying an existingprofile for the consumer, wherein the existing profile identifies theidentity information of the consumer and the payment method.
 63. Thecomputer-implemented method according to claim 56, further comprising:in an instance in which the payment information is on file with themerchant device, generating a form that includes the identityinformation and the email address; and requesting a response from theconsumer.